How to Use the LlamaIndex (AI Data Framework & RAG) MCP in Google ADK

Q: How does Google ADK connect to LlamaIndex (AI Data Framework & RAG)?

You use McpToolset with the HTTP server parameters in your Google ADK code and pass it to your Gemini agent. This exposes the 6 LlamaIndex tools directly to your Gemini model for search and metadata inspection.

Q: Can I restrict which tools Google ADK can access on LlamaIndex (AI Data Framework & RAG)?

Yes, you can use the optional toolnames filter in your Google ADK toolset configuration to restrict access to LlamaIndex. This lets you expose only querypipeline to Gemini while hiding administrative tools like listprojects.

Q: How does LlamaIndex (AI Data Framework & RAG) handle Gemini's long context in Google ADK?

The querypipeline tool fetches the most relevant context chunks from your LlamaIndex vector database. The Google ADK agent then processes these chunks within Gemini's large token window, combining vector search with deep reasoning.

Q: How is my source file metadata protected when using Google ADK with LlamaIndex?

Only the file metadata returned by listfiles and listindexes is passed to your Google ADK agent. All communication is encrypted, and Vinkius uses an ephemeral sandbox to ensure your LlamaIndex pipeline configurations are never stored.

Connect Gemini models to your LlamaIndex RAG pipelines using Google ADK for long-context enterprise search.

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Connect LlamaIndex (AI Data Framework & RAG) MCP to Google ADK

Create your Vinkius account to connect LlamaIndex (AI Data Framework & RAG) to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LlamaIndex (AI Data Framework & RAG) with Google ADK

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Run semantic search queries via the Google ADK MCP Server

The `query_pipeline` tool executes natural language queries against your active LlamaIndex pipelines directly from your Google ADK Gemini agent. This feeds highly relevant context back into Gemini's 1M+ token window for deep reasoning tasks. You set up this MCP Server toolset by passing `McpToolset` to your Google ADK agent constructor. The Gemini agent calls the tool natively, bridging your Google Cloud infrastructure with your LlamaIndex data.

Inspect pipeline configurations and active indexes

The `get_pipeline` tool retrieves the exact configuration details of your LlamaCloud data pipelines for your Google ADK agent. This allows your Gemini agent to verify which chunking strategies are active before executing a search. Your Google ADK agent can also call `list_indexes` to discover active LlamaIndex vector indexes. This dynamic discovery means your Gemini agent doesn't need hardcoded index names inside your BigQuery workflows.

Track ingested files for data compliance

The `list_files` tool lists the raw source files currently ingested by your LlamaIndex pipeline for your Google ADK agent. This lets your Gemini agent verify the presence of compliance documents before answering sensitive enterprise queries. Because Google ADK supports both Stdio and HTTP transports, you can run this MCP Server tool in secure, isolated environments. Your Gemini agent gets a clean list of LlamaIndex files without direct database access.

Setup guide

Set up LlamaIndex (AI Data Framework & RAG) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with LlamaIndex (AI Data Framework & RAG) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="LlamaIndex (AI Data Framework & RAG)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to LlamaIndex (AI Data Framework & RAG) tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LlamaIndex. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LlamaIndex (AI Data Framework & RAG) MCP in Google ADK

You use `McpToolset` with the HTTP server parameters in your Google ADK code and pass it to your Gemini agent. This exposes the 6 LlamaIndex tools directly to your Gemini model for search and metadata inspection.

Yes, you can use the optional `tool_names` filter in your Google ADK toolset configuration to restrict access to LlamaIndex. This lets you expose only `query_pipeline` to Gemini while hiding administrative tools like `list_projects`.

The `query_pipeline` tool fetches the most relevant context chunks from your LlamaIndex vector database. The Google ADK agent then processes these chunks within Gemini's large token window, combining vector search with deep reasoning.

Yes, the Google ADK integration supports both Stdio and HTTP transports for local development. You can run the LlamaIndex MCP server locally on your machine or host it on Vinkius for production deployments.

Only the file metadata returned by `list_files` and `list_indexes` is passed to your Google ADK agent. All communication is encrypted, and Vinkius uses an ephemeral sandbox to ensure your LlamaIndex pipeline configurations are never stored.

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